Literature DB >> 11821862

De novo peptide sequencing and quantitative profiling of complex protein mixtures using mass-coded abundance tagging.

Gerard Cagney1, Andrew Emili.   

Abstract

Proteomic studies require efficient, robust, and practical methods of characterizing proteins present in biological samples. Here we describe an integrated strategy for systematic proteome analysis based on differential guanidination of C-terminal lysine residues on tryptic peptides followed by capillary liquid chromatography-electrospray tandem mass spectrometry. The approach, termed mass-coded abundance tagging (MCAT), facilitates the automated, large-scale, and comprehensive de novo determination of peptide sequence and relative quantitation of proteins in biological samples in a single analysis. MCAT offers marked advantages as compared with previously described methods and is simple, economic, and effective when applied to complex proteomic mixtures. MCAT is used to identify proteins, including polymorphic variants, from complex mixtures and measure variation in protein levels from diverse cell types.

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Year:  2002        PMID: 11821862     DOI: 10.1038/nbt0202-163

Source DB:  PubMed          Journal:  Nat Biotechnol        ISSN: 1087-0156            Impact factor:   54.908


  20 in total

1.  "De novo" peptide sequencing by MALDI-quadrupole-ion trap mass spectrometry: a preliminary study.

Authors:  Wenzhu Zhang; Andrew N Krutchinsky; Brian T Chait
Journal:  J Am Soc Mass Spectrom       Date:  2003-09       Impact factor: 3.109

2.  Dynamic changes in protein-protein interaction and protein phosphorylation probed with amine-reactive isotope tag.

Authors:  Marcus B Smolka; Claudio P Albuquerque; Sheng-hong Chen; Kristina H Schmidt; Xiao X Wei; Richard D Kolodner; Huilin Zhou
Journal:  Mol Cell Proteomics       Date:  2005-06-22       Impact factor: 5.911

Review 3.  Multidimensional protein identification technology (MudPIT): technical overview of a profiling method optimized for the comprehensive proteomic investigation of normal and diseased heart tissue.

Authors:  Thomas Kislinger; Anthony O Gramolini; David H MacLennan; Andrew Emili
Journal:  J Am Soc Mass Spectrom       Date:  2005-08       Impact factor: 3.109

Review 4.  Proteomic technology for biomarker profiling in cancer: an update.

Authors:  Moulay A Alaoui-Jamali; Ying-jie Xu
Journal:  J Zhejiang Univ Sci B       Date:  2006-06       Impact factor: 3.066

Review 5.  Accurate mass measurements in proteomics.

Authors:  Tao Liu; Mikhail E Belov; Navdeep Jaitly; Wei-Jun Qian; Richard D Smith
Journal:  Chem Rev       Date:  2007-07-25       Impact factor: 60.622

6.  Rapid validation of Mascot search results via stable isotope labeling, pair picking, and deconvolution of fragmentation patterns.

Authors:  Samuel L Volchenboum; Kolbrun Kristjansdottir; Donald Wolfgeher; Stephen J Kron
Journal:  Mol Cell Proteomics       Date:  2009-05-11       Impact factor: 5.911

Review 7.  Quantitative strategies to fuel the merger of discovery and hypothesis-driven shotgun proteomics.

Authors:  Kelli G Kline; Greg L Finney; Christine C Wu
Journal:  Brief Funct Genomic Proteomic       Date:  2009-03

8.  Analyzing the cardiac muscle proteome by liquid chromatography-mass spectrometry-based expression proteomics.

Authors:  Anthony O Gramolini; Thomas Kislinger; Peter Liu; David H MacLennan; Andrew Emili
Journal:  Methods Mol Biol       Date:  2007

Review 9.  Insights into MHC class I antigen processing gained from large-scale analysis of class I ligands.

Authors:  Gabor Mester; Vanessa Hoffmann; Stefan Stevanović
Journal:  Cell Mol Life Sci       Date:  2011-03-09       Impact factor: 9.261

10.  Shotgun proteomics in neuroscience.

Authors:  Lujian Liao; Daniel B McClatchy; John R Yates
Journal:  Neuron       Date:  2009-07-16       Impact factor: 17.173

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